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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/1860
Title: Racing game control using reinforcement learning and visual attention
Authors: Vuong Khanh Linh 
Keywords: Attention mechanism;Deep Q-Networks
Issue Date: 2024
Abstract: 
This thesis explores how combining Deep Q-Networks (DQN) with attention mechanisms can improve control in racing games. The aim is to develop a control system that uses reinforcement learning to make better driving decisions. The DQN algorithm helps the agent learn the best actions to take based on game rewards and feedback. By adding an attention mechanism, the agent can focus on important visual elements, like the track and other vehicles, making its decisions more precise. This paper explains how DQN and attention are integrated, including the changes made to the traditional DQN model. Tests show that this improved model performs better in driving accuracy and responsiveness than standard DQN methods. This research shows that combining reinforcement learning with attention mechanisms can make game control systems smarter and more effective.
URI(1): https://epub.vgu.edu.vn/handle/dlibvgu/1860
Rights: Attribution-NonCommercial 4.0 International
Appears in Collections:Computer Science (CS)

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